Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "88" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 24 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 24 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460007 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.538879 | 0.130672 | 0.677350 | 1.291967 | 0.207027 | -0.057314 | 1.110750 | 0.409815 | 0.6179 | 0.6309 | 0.3396 | nan | nan |
| 2459999 | digital_ok | 0.00% | 89.06% | 85.63% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1560 | 0.1458 | 0.0785 | nan | nan |
| 2459998 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.068347 | 0.614976 | 0.573856 | 1.051646 | 0.400820 | -0.640514 | -0.515388 | -0.204411 | 0.6240 | 0.6410 | 0.3618 | nan | nan |
| 2459997 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.034603 | 0.534732 | 0.520387 | 1.234486 | -0.300319 | -0.822789 | -0.185076 | -0.166633 | 0.6326 | 0.6545 | 0.3650 | nan | nan |
| 2459996 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.418641 | 1.405463 | 1.050635 | 1.874652 | -0.159973 | -0.614029 | 1.123712 | 0.510558 | 0.6380 | 0.6540 | 0.3769 | nan | nan |
| 2459995 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.616249 | 0.640743 | 0.590307 | 1.347318 | -0.274257 | 0.092252 | 1.903899 | 0.520012 | 0.6262 | 0.6430 | 0.3709 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.709992 | -0.016304 | 0.545089 | 1.152392 | -0.053457 | -0.572365 | 2.195089 | 0.451284 | 0.6189 | 0.6343 | 0.3692 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.856590 | -0.305623 | 0.287220 | 0.939691 | -0.361786 | -0.310416 | 2.369893 | 0.338884 | 0.6118 | 0.6387 | 0.3786 | nan | nan |
| 2459991 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.963520 | 0.072520 | 0.499654 | 0.976591 | -0.156068 | -0.598760 | 0.982521 | 0.121564 | 0.6379 | 0.6482 | 0.3664 | nan | nan |
| 2459990 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.754019 | -0.261862 | 0.552173 | 0.827547 | 0.070421 | -0.709833 | 0.811485 | -0.267222 | 0.6333 | 0.6459 | 0.3639 | nan | nan |
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.884130 | -0.128300 | 0.473783 | 0.956855 | -0.229543 | -1.154602 | -0.216158 | -0.558171 | 0.6272 | 0.6443 | 0.3676 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.046706 | -0.105540 | 0.452720 | 0.734902 | -0.315315 | -1.037117 | 1.549457 | 0.140153 | 0.6272 | 0.6427 | 0.3598 | nan | nan |
| 2459987 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.243761 | 0.231930 | 0.405142 | 0.990637 | -0.366466 | -0.819042 | -0.120415 | -0.424606 | 0.6383 | 0.6536 | 0.3592 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.162136 | 0.342056 | 0.460501 | 0.912738 | -0.737745 | -0.594656 | 2.561241 | 0.600948 | 0.6564 | 0.6762 | 0.3153 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.549133 | 0.512508 | 0.406291 | 1.015679 | -0.712434 | -0.937028 | 6.767400 | 2.249529 | 0.6355 | 0.6491 | 0.3653 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.828785 | 0.671079 | 0.373718 | 1.122473 | -0.600356 | -0.563662 | 3.383055 | 0.878111 | 0.6541 | 0.6654 | 0.3439 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.414494 | 0.740137 | 0.495731 | 0.848255 | -0.499440 | -0.718718 | 1.743077 | -0.059610 | 0.6658 | 0.6904 | 0.3039 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.249787 | 0.196157 | 0.665556 | 0.895459 | -0.112378 | -0.637698 | 0.232995 | 0.362942 | 0.7251 | 0.7250 | 0.2595 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.605058 | 0.020899 | 0.472270 | 0.719640 | -0.462857 | -0.771746 | 1.829718 | 0.224719 | 0.6382 | 0.6524 | 0.3596 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.002214 | -0.002214 | 0.308481 | 0.790049 | -0.521891 | -0.667896 | 0.876288 | 0.577105 | 0.6855 | 0.6980 | 0.2840 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.211595 | -0.213986 | 0.221833 | 0.627369 | -0.268725 | -0.983642 | 1.008276 | 0.208365 | 0.6309 | 0.6481 | 0.3610 | nan | nan |
| 2459978 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.628786 | -0.121098 | 0.271260 | 0.661137 | -0.300594 | -0.834865 | 0.617558 | -0.116435 | 0.6320 | 0.6478 | 0.3665 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.743980 | 0.335739 | 0.279174 | 0.782715 | -0.364763 | -0.500029 | 2.020132 | 0.626130 | 0.5986 | 0.6167 | 0.3338 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.635152 | 0.547959 | 0.346504 | 0.756359 | -0.195289 | -0.009130 | 0.095072 | -0.356776 | 0.6383 | 0.6543 | 0.3574 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 1.291967 | 0.538879 | 0.130672 | 0.677350 | 1.291967 | 0.207027 | -0.057314 | 1.110750 | 0.409815 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 1.051646 | -0.068347 | 0.614976 | 0.573856 | 1.051646 | 0.400820 | -0.640514 | -0.515388 | -0.204411 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 1.234486 | 0.034603 | 0.534732 | 0.520387 | 1.234486 | -0.300319 | -0.822789 | -0.185076 | -0.166633 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 1.874652 | 0.418641 | 1.405463 | 1.050635 | 1.874652 | -0.159973 | -0.614029 | 1.123712 | 0.510558 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 1.903899 | 0.616249 | 0.640743 | 0.590307 | 1.347318 | -0.274257 | 0.092252 | 1.903899 | 0.520012 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 2.195089 | 0.709992 | -0.016304 | 0.545089 | 1.152392 | -0.053457 | -0.572365 | 2.195089 | 0.451284 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 2.369893 | 0.856590 | -0.305623 | 0.287220 | 0.939691 | -0.361786 | -0.310416 | 2.369893 | 0.338884 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 0.982521 | 0.963520 | 0.072520 | 0.499654 | 0.976591 | -0.156068 | -0.598760 | 0.982521 | 0.121564 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 0.827547 | -0.261862 | 0.754019 | 0.827547 | 0.552173 | -0.709833 | 0.070421 | -0.267222 | 0.811485 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 0.956855 | -0.128300 | 0.884130 | 0.956855 | 0.473783 | -1.154602 | -0.229543 | -0.558171 | -0.216158 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 1.549457 | -0.105540 | 1.046706 | 0.734902 | 0.452720 | -1.037117 | -0.315315 | 0.140153 | 1.549457 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 0.990637 | 0.243761 | 0.231930 | 0.405142 | 0.990637 | -0.366466 | -0.819042 | -0.120415 | -0.424606 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 2.561241 | 0.342056 | -0.162136 | 0.912738 | 0.460501 | -0.594656 | -0.737745 | 0.600948 | 2.561241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 6.767400 | 0.512508 | 0.549133 | 1.015679 | 0.406291 | -0.937028 | -0.712434 | 2.249529 | 6.767400 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 3.383055 | 0.828785 | 0.671079 | 0.373718 | 1.122473 | -0.600356 | -0.563662 | 3.383055 | 0.878111 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 1.743077 | 0.414494 | 0.740137 | 0.495731 | 0.848255 | -0.499440 | -0.718718 | 1.743077 | -0.059610 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 0.895459 | 0.249787 | 0.196157 | 0.665556 | 0.895459 | -0.112378 | -0.637698 | 0.232995 | 0.362942 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 1.829718 | 0.020899 | 0.605058 | 0.719640 | 0.472270 | -0.771746 | -0.462857 | 0.224719 | 1.829718 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 0.876288 | -0.002214 | 0.002214 | 0.790049 | 0.308481 | -0.667896 | -0.521891 | 0.577105 | 0.876288 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 1.008276 | 0.211595 | -0.213986 | 0.221833 | 0.627369 | -0.268725 | -0.983642 | 1.008276 | 0.208365 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 0.661137 | -0.121098 | 0.628786 | 0.661137 | 0.271260 | -0.834865 | -0.300594 | -0.116435 | 0.617558 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | ee Temporal Discontinuties | 2.020132 | 0.743980 | 0.335739 | 0.279174 | 0.782715 | -0.364763 | -0.500029 | 2.020132 | 0.626130 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 88 | N09 | digital_ok | nn Power | 0.756359 | 0.547959 | 0.635152 | 0.756359 | 0.346504 | -0.009130 | -0.195289 | -0.356776 | 0.095072 |